econ401 final project R Notebook

Libraries:

library(tidyverse)
library(readr)
library(rvest)
library(stats)
library(readxl)
library(dplyr)
library(stringr)
library(ggplot2)
library(ggthemes)
library(stringr)
library(data.table)
library(geojsonio)
library(leaflet)
library(rgdal)
library(haven)

Data:

# GDP up to Feb 2020
# https://ihsmarkit.com/products/us-monthly-gdp-index.html
gdp.index.data <- readxl::read_xlsx('US-Monthly-GDP-History-Data.xlsx', sheet = 3)
gdp.index <- gdp.index.data
colnames(gdp.index)[1] <- "Y_M"
year.month <- str_split_fixed(gdp.index$Y_M, ' - ', 2)
colnames(year.month) <- c('Year', 'Month')
gdp.index <- cbind(year.month, gdp.index[, -1])
gdp.annual <- gdp.index %>%
  group_by(Year) %>%
  summarize(MaxGDP = max(`Monthly Real GDP Index`),
            MinGDP = min(`Monthly Real GDP Index`))

# https://nces.ed.gov/programs/digest/d18/tables/dt18_306.10.asp
enrollment.data <- read_xls('tabn306.10.xls')
enrollment <- enrollment.data[1:12]
# enrollment is in thousands
enrollment <- enrollment[-c(1, 3, 15, 27, 39, 51, 63, 75, 99, 111, 123, 135:139), ]
col1 <- data.frame(str_remove_all(enrollment[[1]], '\\.'), stringsAsFactors = FALSE)
col1[2, 1] <- "All_Students"
enrollment <- cbind(col1, enrollment[, -1])
enrollment <- t(enrollment)
rownames(enrollment) <- c()
colnames(enrollment) <- enrollment[1, ]
enrollment <- data.frame(enrollment)
colnames(enrollment)[1] <- 'Year'
enrollment <- enrollment[-1, ]
Years <- as.numeric(str_extract(enrollment$Year, "[:digit:]{4}"))
enrollment <- cbind(Years, enrollment[, -1])
enrollment <- data.frame(lapply(enrollment, function(x){ 
  gsub("---", NA, x)
}))
str(enrollment)

enrollment1 <- enrollment[, 1:2]
gdp.annual1 <- gdp.annual
all.students <- as.numeric(enrollment[4:11, 2])

gdp.annual$Year <- as.factor(gdp.annual$Year)
enrollment1$Years <- as.factor(enrollment1$Years)
enrollment1$All_Students <- as.numeric(as.character(enrollment1$All_Students))

Model 3:

First making a graph of annual GDP highs and lows – maybe a temporary proxy for recessions?

Annual enrollment graph:

Join enrollment data and gdp data to create linear model test:

Graph it?

College Proximity Question 5/3: (Reading in Ivy’s data)

cz_college <- read_dta("cz_college.dta")
cz <- read_dta('cz.dta')
colleges <- read_dta('colleges.dta')
mobility.results <- read_xlsx('mobility_results.xlsx')

Read in/create mobility data: (Trends in Mobility: Commuting Zone Intergenerational Mobility Estimates by Birth Cohort) https://opportunityinsights.org/data/?geographic_level=101&topic=0&paper_id=0#resource-listing

grep('ncollege', colnames(cz.mobility))

[1] 2132

Read in geojson file:

Commuting zones on the map (cz.geojson@data) are in 1990s format. They need to be converted so our post-2000 data can be connected to the shapefiles: (https://www.ers.usda.gov/data-products/commuting-zones-and-labor-market-areas/)

Try to run some lms:

lm1 <- lm()

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